Continuous Glucose Monitors and Activity Trackers to Inform Insulin Dosing in Type 1 Diabetes: The University of Virginia Contribution
Continuous Glucose Monitors and Activity Trackers to Inform Insulin Dosing in Type 1 Diabetes: The University of Virginia Contribution
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DOI：
10.3390/s19245386
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发表时间：
2019-12-02
期刊：
影响因子：
3.9
通讯作者：
Breton, Marc D.
中科院分区：
文献类型：
--
作者：
Fabris, Chiara;Ozaslan, Basak;Breton, Marc D.
目的：1 型糖尿病 (T1D) 中胰岛素剂量不理想通常与影响胰岛素敏感性 (IS) 的各种心理行为和生理因素驱动的随时间变化的胰岛素需求有关。其中，体力活动已被广泛认为是运动期间和运动后 IS 改变的触发因素，但可用于管理 T1D 结构化和（甚至更多）非结构化活动的指示有限。在这项工作中，我们提出了两种方法，通过可穿戴传感器的生物信号通知胰岛素剂量，以改善 1D 患者的血糖控制。研究设计和方法：这些方法利用了连续血糖监测仪 (CGM) 和活动跟踪器。第一种方法利用CGM记录实时估计IS并根据个人的胰岛素需求调整胰岛素剂量；第二种方法使用步数计数数据来通知推注计算以及最近进行的（结构化或非结构化）体力活动的残余降糖效果。这些方法在弗吉尼亚大学/帕多瓦 T1D 模拟器中进行了计算机测试。在 IS 增加/减少的情况下（研究 1）和 1 小时的锻炼后（研究 2），部署了标准推注计算器和拟议的“智能”系统来控制一顿饭。根据不同血糖范围和低/高血糖指数（LBGI/HBGI）所花费的时间来评估餐后血糖控制，并在给药策略之间进行比较。结果：在研究 1 中，CGM 通知系统可以减少 IS 增加时的低血糖暴露（时间百分比 < 70 mg/dL：6.1% 与 9.9%；LBGI：1.9 与 3.2）以及 IS 降低时的高血糖暴露（时间百分比 > 180 mg/dL：14.6% 与 18.3%；HBGI： 3.0 与 3.9），趋于最优控制。在研究 2 中，计步通知系统可以减少低血糖（< 70 mg/dL 的时间百分比：3.9% 与 13.4%；LBGI：1.7 与 3.2），但代价是高血糖暴露略有增加（> 180 mg/dL 的时间百分比：11.9% 与 7.5%；HBGI：2.4 与1.5）。结论：我们提出并通过计算机验证了 1 型糖尿病中餐时胰岛素智能剂量的两种方法。如果在整体中观察，这两种算法为 T1D 患者提供了替代方案，以改善胰岛素剂量，适应多种治疗方案。未来的工作将致力于测试这些方法在自由生活条件下的安全性和有效性。
Objective: Suboptimal insulin dosing in type 1 diabetes (T1D) is frequently associated with time-varying insulin requirements driven by various psycho-behavioral and physiological factors influencing insulin sensitivity (IS). Among these, physical activity has been widely recognized as a trigger of altered IS both during and following the exercise effort, but limited indication is available for the management of structured and (even more) unstructured activity in T1D. In this work, we present two methods to inform insulin dosing with biosignals from wearable sensors to improve glycemic control in individuals with T1D. Research Design and Methods: Continuous glucose monitors (CGM) and activity trackers are leveraged by the methods. The first method uses CGM records to estimate IS in real time and adjust the insulin dose according to a person's insulin needs; the second method uses step count data to inform the bolus calculation with the residual glucose-lowering effects of recently performed (structured or unstructured) physical activity. The methods were tested in silico within the University of Virginia/Padova T1D Simulator. A standard bolus calculator and the proposed "smart" systems were deployed in the control of one meal in presence of increased/decreased IS (Study 1) and following a 1-hour exercise bout (Study 2). Postprandial glycemic control was assessed in terms of time spent in different glycemic ranges and low/high blood glucose indices (LBGI/HBGI), and compared between the dosing strategies. Results: In Study 1, the CGM-informed system allowed to reduce exposure to hypoglycemia in presence of increased IS (percent time < 70 mg/dL: 6.1% versus 9.9%; LBGI: 1.9 versus 3.2) and exposure to hyperglycemia in presence of decreased IS (percent time > 180 mg/dL: 14.6% versus 18.3%; HBGI: 3.0 versus 3.9), tending toward optimal control. In Study 2, the step count-informed system allowed to reduce hypoglycemia (percent time < 70 mg/dL: 3.9% versus 13.4%; LBGI: 1.7 versus 3.2) at the cost of a minor increase in exposure to hyperglycemia (percent time > 180 mg/dL: 11.9% versus 7.5%; HBGI: 2.4 versus 1.5). Conclusions: We presented and validated in silico two methods for the smart dosing of prandial insulin in T1D. If seen within an ensemble, the two algorithms provide alternatives to individuals with T1D for improving insulin dosing accommodating a large variety of treatment options. Future work will be devoted to test the safety and efficacy of the methods in free-living conditions.